详解分类评价指标和回归评价指标以及Python代码实现

#调用sklearn库中的指标求解from sklearn import metricsfrom sklearn.metrics import precision_recall_curvefrom sklearn.metrics import average_precision_scorefrom sklearn.metrics import accuracy_score#给出分类结果y_pred = [0, 1, 0, 0]

y_true = [0, 1, 1, 1]

print("accuracy_score:", accuracy_score(y_true, y_pred))

print("precision_score:", metrics.precision_score(y_true, y_pred))

print("recall_score:", metrics.recall_score(y_true, y_pred))

print("f1_score:", metrics.f1_score(y_true, y_pred))

print("f0.5_score:", metrics.fbeta_score(y_true, y_pred, beta=0.5))

print("f2_score:", metrics.fbeta_score(y_true, y_pred, beta=2.0))

#precision和recall的求法如上

#主要介绍一下python画图的库

import matplotlib.pyplot ad plt

#主要用于矩阵运算的库

import numpy as np#导入iris数据及训练见前一博文

...

#加入800个噪声特征，增加图像的复杂度

#将150*800的噪声特征矩阵与150*4的鸢尾花数据集列合并

X = np.c_[X, np.random.RandomState(0).randn(n_samples, 200*n_features)]

#计算precision，recall得到数组

for i in range(n_classes):

    #计算三类鸢尾花的评价指标， _作为临时的名称使用

    precision[i], recall[i], _ = precision_recall_curve(y_test[:, i], y_score[:,i])#plot作图plt.clf()

for i in range(n_classes):

    plt.plot(recall[i], precision[i])

plt.xlim([0.0, 1.0])

plt.ylim([0.0, 1.05])

plt.xlabel("Recall")

plt.ylabel("Precision")

plt.show()

from sklearn.metrics import log_loss

log_loss(y_true, y_pred)from scipy.stats import pearsonr

pearsonr(rater1, rater2)from sklearn.metrics import cohen_kappa_score

cohen_kappa_score(rater1, rater2)